A large number of static link prediction methods have been proposed, and these methods can be divided into three categories[1]}. The first category is the method based on probability statistics[2]}. The basic idea of these methods is to build a parametric probabilistic model and use optimization strategies such as maximum likelihood estimation to find the optimal parameters. This type of model usually acquires satisfying results by assuming that the network has known structures or obeys specific distributions. But usually the great computational cost makes it not suitable for large-scale network, and representative works are like [3]}, [4]}, [5]}. The second category is machine learning-based methods. The link prediction problem in the network can be regarded as a classification problem in machine learning[6]}, and the related methods work on massive training data to achieve high prediction accuracy in large-scale networks though explainable features are difficult to be extracted. Furthermore, inspired by the superiority of deep learning and graph representation learning in capturing node feature representations[7]}, the link prediction task can be transformed into computing distances between nodes to reveal the underlying correlation. The advantage of this type of method is that with the iterative update of representation learning algorithms, such as deepwalk[8]}, node2vec[9]} and their derivatives, the link prediction accuracy can be gradually improved, but the prediction mechanism is difficult to explain in an explicit way. The third category is the similarity-based method[10]}, which is based on the assumption that the connection probability of nodes is positively correlated with the similarity between them[11]}, [12]}. Such methods assign a score to each pair of nodes by defining a similarity function, and higher scored node pairs will have more potential to be linked together.
